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Full-Text Articles in Engineering
Subsidence Measurement And Dsm Extraction Of Ifsar Data Using Anisotropic Diffusion And Wavelet Denoising Filters, Kenneth Sartor, Josef De Vaughn Allen, Emile Ganthier, Mark Rahmes, Gnana Bhaskar Tenali, Samuel Peter Kozaitis
Subsidence Measurement And Dsm Extraction Of Ifsar Data Using Anisotropic Diffusion And Wavelet Denoising Filters, Kenneth Sartor, Josef De Vaughn Allen, Emile Ganthier, Mark Rahmes, Gnana Bhaskar Tenali, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
The most commonly used smoothing algorithms for complex data processing are low pass filters. Unfortunately, an undesired side effect of the aforementioned techniques is the blurring of scene discontinuities in the interferogram. For Digital Surface Map (DSM) extraction and subsidence measurement, the smoothing of the scene discontinuities can cause inaccuracy in the final product. Our goal is to perform spatially non-uniform smoothing to overcome the aforementioned disadvantages. We achieve this by using an Anisotropic Non-Linear Diffuser (ANDI). Here, in this paper we will show the utility of ANDI filtering on simulated and actual Interferometric Synthetic Aperture Radar (IFSAR) data for …
Signal-To-Noise Ratio For Cross-Sensor Fusion Approach, Samuel Peter Kozaitis, Michel Ouendeno
Signal-To-Noise Ratio For Cross-Sensor Fusion Approach, Samuel Peter Kozaitis, Michel Ouendeno
Electrical Engineering and Computer Science Faculty Publications
We determined the signal-to-noise ratios for an image fusion approach that is suitable for application to systems with disparate sensors. We found there was reconstruction error present when the forward and reverse transforms for image fusion give perfect reconstruction.
Wavelet-Based Fusion Approach Using Unique Reconstruction Approach, Michel Ouendeno, Samuel Peter Kozaitis
Wavelet-Based Fusion Approach Using Unique Reconstruction Approach, Michel Ouendeno, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
We used measures based on entropy to evaluate a method designed to fuse imagery from different sensor types. The method uses different forward transforms of input images and a common transform to reconstruct the final result. We attempted to examine the link between the error in a reconstructed result and its associated entropy.
Image Fusion For Improved Perception, Michel Ouendeno, Samuel Peter Kozaitis
Image Fusion For Improved Perception, Michel Ouendeno, Samuel Peter Kozaitis
Electrical Engineering and Computer Science Faculty Publications
We developed a method to fuse imagery for from different sensor types. The core of our method uses different forward transforms of input images and a common transform to reconstruct the final result. When measuring the entropy and power of the fused result, we found that our method gave generally better results when compared to a more conventional approach. Our method could form the basis of a new image fusion approach because it offers results not possible with a conventional approach.
Cross-Sensor Fusion Of Imagery For Improved Information Extraction, Samuel Peter Kozaitis, Michel Ouendeno
Cross-Sensor Fusion Of Imagery For Improved Information Extraction, Samuel Peter Kozaitis, Michel Ouendeno
Electrical Engineering and Computer Science Faculty Publications
We combined cross-sensor data that leads to improved extraction of information from disparate sensors. We presented a new method for signal fusion that uses different transforms for the forward transforms of two images and a common transform for the inverse. When using a fusion rule that selects the maximum value between images, we were able to transfer more energy to the result using our method. Our method could form the basis of a new image fusion approach because it offers a way to transfer more energy to the result not possible with a conventional approach.